| IHME | WHO | Dif | % difference (WHO / IHME) | % difference (IHME / WHO) | ||
|---|---|---|---|---|---|---|
| Total | HIV+TB only | 211604 | 389042 | 177438.13321 | 83.85% | -45.61% |
| TB only | 1111312 | 1379440 | 268128.44955 | 24.13% | -19.44% | |
| Total TB | 1322916 | 1768482 | 445566.58275 | 33.68% | -25.19% | |
| Adults | HIV+TB only | 177567 | 348026 | 170458.90473 | 96% | -48.98% |
| TB only | 1075691 | 1210620 | 134929.12946 | 12.54% | -11.15% | |
| Total TB | 1253257 | 1558645 | 305388.03419 | 24.37% | -19.59% | |
| Children | HIV+TB only | 34037 | 41016 | 6979.22848 | 20.5% | -17.02% |
| TB only | 35621 | 168821 | 133199.32009 | 373.93% | -78.9% | |
| Total TB | 69659 | 209837 | 140178.54857 | 201.24% | -66.8% | |
| Female | HIV+TB only | 78110 | 143496 | 65386.51804 | 83.71% | -45.57% |
| TB only | 367764 | 352488 | 15276.43876 | -4.15% | 4.33% | |
| Total TB | 445874 | 495984 | 50110.07929 | 11.24% | -10.1% | |
| Male | HIV+TB only | 99457 | 204471 | 105013.90757 | 105.59% | -51.36% |
| TB only | 707927 | 858132 | 150205.56821 | 21.22% | -17.5% | |
| Total TB | 807383 | 1062603 | 255219.47578 | 31.61% | -24.02% | |
| AMR | HIV+TB only | 579 | 620 | 41.31917 | 7.14% | -6.66% |
| TB only | 2036 | 1914 | 122.37010 | -6.01% | 6.39% | |
| Total TB | 2615 | 2534 | 81.05093 | -3.1% | 3.2% | |
| EMR | HIV+TB only | 165 | 533 | 368.30203 | 223.05% | -69.04% |
| TB only | 14658 | 14572 | 85.51575 | -0.58% | 0.59% | |
| Total TB | 14823 | 15106 | 282.78629 | 1.91% | -1.87% | |
| EUR | HIV+TB only | 212 | 374 | 161.68749 | 76.15% | -43.23% |
| TB only | 2383 | 2999 | 615.93832 | 25.85% | -20.54% | |
| Total TB | 2595 | 3373 | 777.62581 | 29.97% | -23.06% | |
| SEA | HIV+TB only | 19310 | 28870 | 9560.04060 | 49.51% | -33.11% |
| TB only | 333250 | 345889 | 12639.43214 | 3.79% | -3.65% | |
| Total TB | 352560 | 374759 | 22199.47275 | 6.3% | -5.92% | |
| WPR | HIV+TB only | 2057 | 2010 | 47.13348 | -2.29% | 2.35% |
| TB only | 39055 | 28351 | 10704.20283 | -27.41% | 37.76% | |
| Total TB | 41112 | 30361 | 10751.33632 | -26.15% | 35.41% |
Table with model output for estimating likelihood or magnitude of difference in estimates by HIV, age, sex, and region.
This section is unfinished.
Rankings of highest absolute and standardized differences for IHME and WHO.
Rankings of highest absolute and standardized differences for IHME and WHO.
The below scatterplot shows the correlation between WHO (x-axis) estimates and IHME (y-axis) estimates, with each point colored by its (WHO-defined) region.
In the following four charts, Libya has been excluded as an outlier.
Linear regression to estimate effect of prevalence survey on absolute difference in cases (WHO minus IHME), adjusting for region.
95% confidence intervals
Linear regression to estimate effect of prevalence survey on adjusted standardized difference in cases, adjusting for region.
95% confidence intervals
(Unfinished)
Correlation of adjusted stand diff with a) HIV prevalence, CDR by both, CFR, MDR prevalence.
cor(df$adjusted_stand_dif, df$newrel_hivpos, use = 'complete.obs')
[1] 0.09204849
cor(df$adjusted_stand_dif, df$gb_c_cdr, use = 'complete.obs')
[1] -0.3688292
cor(df$adjusted_stand_dif, df$cdr_ihme, use = 'complete.obs')
[1] 0.4283954
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014, use = 'complete.obs')
[1] -0.1353054
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014, use = 'complete.obs')
[1] -0.1171681
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015, use = 'complete.obs')
[1] -0.1586533
cor(df$adjusted_stand_dif, df$case_fatality_rate_2014_new, use = 'complete.obs')
[1] -0.1587409
cor(df$adjusted_stand_dif, df$case_fatality_rate_2012_to_2014_new, use = 'complete.obs')
[1] -0.1147299
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_new, use = 'complete.obs')
[1] -0.1760415
cor(df$adjusted_stand_dif, df$case_fatality_rate_2015_adjusted, use = 'complete.obs')
[1] -0.1825428
cor(df$adjusted_stand_dif, df$p_mdr_new, use = 'complete.obs')
[1] 0.05559062
cor(df$adjusted_stand_dif, df$reported_mdr, use = 'complete.obs')
[1] -0.0131539
Does region affect likelihood of having a prevalence survey?
xt <- table(df$prevsurvey, df$who_region)
xt
AFR AMR EMR EUR SEA WPR
0 37 37 20 52 8 22
1 10 0 2 0 3 4
chisq.test(xt)
Pearson's Chi-squared test
data: xt
X-squared = 21.511, df = 5, p-value = 0.0006482
Does having a prev survey affect the adjusted stand diff?
t.test(x = df$adjusted_stand_dif[df$prevsurvey == 0],
y = df$adjusted_stand_dif[df$prevsurvey == 1])
Welch Two Sample t-test
data: df$adjusted_stand_dif[df$prevsurvey == 0] and df$adjusted_stand_dif[df$prevsurvey == 1]
t = -2.1643, df = 21.066, p-value = 0.04207
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-46.808826 -0.938917
sample estimates:
mean of x mean of y
-5.42558 18.44829